Overview

Dataset statistics

Number of variables23
Number of observations12199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory184.0 B

Variable types

Numeric18
Categorical5

Alerts

Administrative is highly overall correlated with Administrative_Duration and 2 other fieldsHigh correlation
Administrative_Duration is highly overall correlated with Administrative and 2 other fieldsHigh correlation
Informational is highly overall correlated with Informational_Duration and 1 other fieldsHigh correlation
Informational_Duration is highly overall correlated with Informational and 1 other fieldsHigh correlation
ProductRelated is highly overall correlated with ProductRelated_Duration and 1 other fieldsHigh correlation
ProductRelated_Duration is highly overall correlated with ProductRelatedHigh correlation
BounceRates is highly overall correlated with ExitRatesHigh correlation
ExitRates is highly overall correlated with ProductRelated and 1 other fieldsHigh correlation
OperatingSystems is highly overall correlated with VisitorType_OtherHigh correlation
Browser is highly overall correlated with VisitorType_OtherHigh correlation
Administrative_Proportion is highly overall correlated with Administrative and 2 other fieldsHigh correlation
Informational_Proportion is highly overall correlated with Informational and 1 other fieldsHigh correlation
ProductRelated_Proportion is highly overall correlated with Administrative and 2 other fieldsHigh correlation
VisitorType_New_Visitor is highly overall correlated with VisitorType_Returning_VisitorHigh correlation
VisitorType_Other is highly overall correlated with OperatingSystems and 1 other fieldsHigh correlation
VisitorType_Returning_Visitor is highly overall correlated with VisitorType_New_VisitorHigh correlation
VisitorType_Other is highly imbalanced (94.2%)Imbalance
Administrative has 5637 (46.2%) zerosZeros
Administrative_Duration has 5637 (46.2%) zerosZeros
Informational has 9568 (78.4%) zerosZeros
Informational_Duration has 9568 (78.4%) zerosZeros
BounceRates has 5518 (45.2%) zerosZeros
PageValues has 9469 (77.6%) zerosZeros
SpecialDay has 10950 (89.8%) zerosZeros
Administrative_Proportion has 5637 (46.2%) zerosZeros
Informational_Proportion has 9568 (78.4%) zerosZeros

Reproduction

Analysis started2024-04-14 12:00:50.970856
Analysis finished2024-04-14 12:02:52.081454
Duration2 minutes and 1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Administrative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3400279
Minimum0
Maximum27
Zeros5637
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:52.612648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3308512
Coefficient of variation (CV)1.4234237
Kurtosis4.6384984
Mean2.3400279
Median Absolute Deviation (MAD)1
Skewness1.9464873
Sum28546
Variance11.09457
MonotonicityNot monotonic
2024-04-14T20:02:53.034949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 5637
46.2%
1 1354
 
11.1%
2 1114
 
9.1%
3 915
 
7.5%
4 765
 
6.3%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.8%
8 287
 
2.4%
9 225
 
1.8%
Other values (17) 557
 
4.6%
ValueCountFrequency (%)
0 5637
46.2%
1 1354
 
11.1%
2 1114
 
9.1%
3 915
 
7.5%
4 765
 
6.3%
5 575
 
4.7%
6 432
 
3.5%
7 338
 
2.8%
8 287
 
2.4%
9 225
 
1.8%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 1
 
< 0.1%
24 4
 
< 0.1%
23 3
 
< 0.1%
22 4
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 6
 
< 0.1%
18 12
0.1%
17 16
0.1%

Administrative_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3336
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.697555
Minimum0
Maximum3398.75
Zeros5637
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:53.472376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q394.75
95-th percentile352.23429
Maximum3398.75
Range3398.75
Interquartile range (IQR)94.75

Descriptive statistics

Standard deviation177.52119
Coefficient of variation (CV)2.172907
Kurtosis50.121291
Mean81.697555
Median Absolute Deviation (MAD)9
Skewness5.5913496
Sum996628.47
Variance31513.773
MonotonicityNot monotonic
2024-04-14T20:02:53.957544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5637
46.2%
1 135
 
1.1%
4 56
 
0.5%
5 53
 
0.4%
7 45
 
0.4%
11 42
 
0.3%
6 41
 
0.3%
14 37
 
0.3%
9 35
 
0.3%
15 33
 
0.3%
Other values (3326) 6085
49.9%
ValueCountFrequency (%)
0 5637
46.2%
1 135
 
1.1%
1.333333333 1
 
< 0.1%
2 15
 
0.1%
3 26
 
0.2%
3.5 4
 
< 0.1%
4 56
 
0.5%
4.333333333 1
 
< 0.1%
4.5 2
 
< 0.1%
4.75 1
 
< 0.1%
ValueCountFrequency (%)
3398.75 1
< 0.1%
2720.5 1
< 0.1%
2657.318056 1
< 0.1%
2629.253968 1
< 0.1%
2407.42381 1
< 0.1%
2156.166667 1
< 0.1%
2137.112745 1
< 0.1%
2086.75 1
< 0.1%
2047.234848 1
< 0.1%
1951.279141 1
< 0.1%

Informational
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50897615
Minimum0
Maximum24
Zeros9568
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:54.379984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2758804
Coefficient of variation (CV)2.5067587
Kurtosis26.646175
Mean0.50897615
Median Absolute Deviation (MAD)0
Skewness4.0131
Sum6209
Variance1.6278707
MonotonicityNot monotonic
2024-04-14T20:02:54.786200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 9568
78.4%
1 1041
 
8.5%
2 728
 
6.0%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
9 15
 
0.1%
8 14
 
0.1%
Other values (7) 18
 
0.1%
ValueCountFrequency (%)
0 9568
78.4%
1 1041
 
8.5%
2 728
 
6.0%
3 380
 
3.1%
4 222
 
1.8%
5 99
 
0.8%
6 78
 
0.6%
7 36
 
0.3%
8 14
 
0.1%
9 15
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
 
< 0.1%
11 1
 
< 0.1%
10 7
 
0.1%
9 15
0.1%
8 14
 
0.1%
7 36
0.3%

Informational_Duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1258
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.861109
Minimum0
Maximum2549.375
Zeros9568
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:55.223639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile199
Maximum2549.375
Range2549.375
Interquartile range (IQR)0

Descriptive statistics

Standard deviation141.45298
Coefficient of variation (CV)4.0576155
Kurtosis75.502701
Mean34.861109
Median Absolute Deviation (MAD)0
Skewness7.5387683
Sum425270.67
Variance20008.944
MonotonicityNot monotonic
2024-04-14T20:02:55.692944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9568
78.4%
1 229
 
1.9%
9 33
 
0.3%
7 26
 
0.2%
10 26
 
0.2%
6 26
 
0.2%
13 23
 
0.2%
12 23
 
0.2%
8 22
 
0.2%
16 22
 
0.2%
Other values (1248) 2201
 
18.0%
ValueCountFrequency (%)
0 9568
78.4%
1 229
 
1.9%
1.5 1
 
< 0.1%
2 11
 
0.1%
2.5 1
 
< 0.1%
3 16
 
0.1%
3.5 1
 
< 0.1%
4 17
 
0.1%
5 18
 
0.1%
5.5 3
 
< 0.1%
ValueCountFrequency (%)
2549.375 1
< 0.1%
2256.916667 1
< 0.1%
2252.033333 1
< 0.1%
2195.3 1
< 0.1%
2166.5 1
< 0.1%
2050.433333 1
< 0.1%
1949.166667 1
< 0.1%
1830.5 1
< 0.1%
1779.166667 1
< 0.1%
1778 1
< 0.1%

ProductRelated
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.061398
Minimum0
Maximum705
Zeros32
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:56.162043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median18
Q338
95-th percentile110
Maximum705
Range705
Interquartile range (IQR)30

Descriptive statistics

Standard deviation44.59895
Coefficient of variation (CV)1.3910482
Kurtosis31.067017
Mean32.061398
Median Absolute Deviation (MAD)13
Skewness4.3331277
Sum391117
Variance1989.0663
MonotonicityNot monotonic
2024-04-14T20:02:56.771335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 504
 
4.1%
3 458
 
3.8%
2 458
 
3.8%
4 404
 
3.3%
6 396
 
3.2%
7 391
 
3.2%
5 382
 
3.1%
8 370
 
3.0%
10 330
 
2.7%
9 317
 
2.6%
Other values (301) 8189
67.1%
ValueCountFrequency (%)
0 32
 
0.3%
1 504
4.1%
2 458
3.8%
3 458
3.8%
4 404
3.3%
5 382
3.1%
6 396
3.2%
7 391
3.2%
8 370
3.0%
9 317
2.6%
ValueCountFrequency (%)
705 1
< 0.1%
686 1
< 0.1%
584 1
< 0.1%
534 1
< 0.1%
518 1
< 0.1%
517 1
< 0.1%
501 1
< 0.1%
486 1
< 0.1%
470 1
< 0.1%
449 1
< 0.1%

ProductRelated_Duration
Real number (ℝ)

HIGH CORRELATION 

Distinct9551
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1207.6246
Minimum0
Maximum63973.522
Zeros32
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:57.491109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1193.70833
median609.54167
Q31477.5648
95-th percentile4313.4514
Maximum63973.522
Range63973.522
Interquartile range (IQR)1283.8564

Descriptive statistics

Standard deviation1919.8562
Coefficient of variation (CV)1.589779
Kurtosis136.6662
Mean1207.6246
Median Absolute Deviation (MAD)502.54167
Skewness7.2529886
Sum14731813
Variance3685848
MonotonicityNot monotonic
2024-04-14T20:02:58.115996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 594
 
4.9%
0 32
 
0.3%
17 21
 
0.2%
8 17
 
0.1%
11 17
 
0.1%
15 16
 
0.1%
19 15
 
0.1%
22 15
 
0.1%
12 15
 
0.1%
7 14
 
0.1%
Other values (9541) 11443
93.8%
ValueCountFrequency (%)
0 32
 
0.3%
0.5 1
 
< 0.1%
1 594
4.9%
2.333333333 1
 
< 0.1%
2.666666667 1
 
< 0.1%
3 5
 
< 0.1%
4 10
 
0.1%
5 13
 
0.1%
5.333333333 1
 
< 0.1%
6 5
 
< 0.1%
ValueCountFrequency (%)
63973.52223 1
< 0.1%
43171.23338 1
< 0.1%
29970.46597 1
< 0.1%
27009.85943 1
< 0.1%
24844.1562 1
< 0.1%
23888.81 1
< 0.1%
23342.08205 1
< 0.1%
23050.10414 1
< 0.1%
21857.04648 1
< 0.1%
21672.24425 1
< 0.1%

BounceRates
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1872
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020283607
Minimum0
Maximum0.2
Zeros5518
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:58.694066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002898551
Q30.016666667
95-th percentile0.14285714
Maximum0.2
Range0.2
Interquartile range (IQR)0.016666667

Descriptive statistics

Standard deviation0.045097002
Coefficient of variation (CV)2.2233226
Kurtosis9.4226873
Mean0.020283607
Median Absolute Deviation (MAD)0.002898551
Skewness3.1736704
Sum247.43972
Variance0.0020337396
MonotonicityNot monotonic
2024-04-14T20:02:59.209632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5518
45.2%
0.2 570
 
4.7%
0.066666667 134
 
1.1%
0.028571429 115
 
0.9%
0.05 113
 
0.9%
0.033333333 101
 
0.8%
0.025 100
 
0.8%
0.016666667 99
 
0.8%
0.1 98
 
0.8%
0.04 96
 
0.8%
Other values (1862) 5255
43.1%
ValueCountFrequency (%)
0 5518
45.2%
2.73 × 10-51
 
< 0.1%
3.35 × 10-51
 
< 0.1%
3.83 × 10-51
 
< 0.1%
3.94 × 10-51
 
< 0.1%
7.09 × 10-51
 
< 0.1%
7.27 × 10-51
 
< 0.1%
7.5 × 10-51
 
< 0.1%
8.01 × 10-51
 
< 0.1%
8.08 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.2 570
4.7%
0.183333333 1
 
< 0.1%
0.18 4
 
< 0.1%
0.176923077 1
 
< 0.1%
0.175 1
 
< 0.1%
0.166666667 4
 
< 0.1%
0.164285714 1
 
< 0.1%
0.164230769 1
 
< 0.1%
0.161904762 1
 
< 0.1%
0.16 3
 
< 0.1%

ExitRates
Real number (ℝ)

HIGH CORRELATION 

Distinct4776
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041389112
Minimum0
Maximum0.2
Zeros76
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:02:59.756841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004545455
Q10.014222579
median0.025
Q30.048466026
95-th percentile0.166875
Maximum0.2
Range0.2
Interquartile range (IQR)0.034243447

Descriptive statistics

Standard deviation0.046044765
Coefficient of variation (CV)1.112485
Kurtosis4.6746263
Mean0.041389112
Median Absolute Deviation (MAD)0.013888889
Skewness2.2388963
Sum504.90578
Variance0.0021201204
MonotonicityNot monotonic
2024-04-14T20:03:00.303694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 580
 
4.8%
0.1 338
 
2.8%
0.05 329
 
2.7%
0.033333333 291
 
2.4%
0.066666667 267
 
2.2%
0.025 224
 
1.8%
0.04 214
 
1.8%
0.016666667 181
 
1.5%
0.02 167
 
1.4%
0.022222222 152
 
1.2%
Other values (4766) 9456
77.5%
ValueCountFrequency (%)
0 76
0.6%
0.000175593 1
 
< 0.1%
0.000250438 1
 
< 0.1%
0.000262123 1
 
< 0.1%
0.000263158 1
 
< 0.1%
0.000292398 1
 
< 0.1%
0.000409836 1
 
< 0.1%
0.000446429 1
 
< 0.1%
0.000468384 1
 
< 0.1%
0.000480769 1
 
< 0.1%
ValueCountFrequency (%)
0.2 580
4.8%
0.192307692 1
 
< 0.1%
0.188888889 2
 
< 0.1%
0.186666667 4
 
< 0.1%
0.183333333 2
 
< 0.1%
0.18034188 1
 
< 0.1%
0.18 3
 
< 0.1%
0.177777778 5
 
< 0.1%
0.175 6
 
< 0.1%
0.173809524 1
 
< 0.1%

PageValues
Real number (ℝ)

ZEROS 

Distinct2704
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9525002
Minimum0
Maximum361.76374
Zeros9469
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:00.834880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38.311638
Maximum361.76374
Range361.76374
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.657792
Coefficient of variation (CV)3.1344462
Kurtosis64.967424
Mean5.9525002
Median Absolute Deviation (MAD)0
Skewness6.3494437
Sum72614.549
Variance348.11318
MonotonicityNot monotonic
2024-04-14T20:03:01.319566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9469
77.6%
53.988 6
 
< 0.1%
42.29306752 3
 
< 0.1%
59.988 2
 
< 0.1%
16.1585582 2
 
< 0.1%
44.89345937 2
 
< 0.1%
14.1273698 2
 
< 0.1%
34.03997536 2
 
< 0.1%
10.99901844 2
 
< 0.1%
58.9241766 2
 
< 0.1%
Other values (2694) 2707
 
22.2%
ValueCountFrequency (%)
0 9469
77.6%
0.038034542 1
 
< 0.1%
0.067049546 1
 
< 0.1%
0.093546949 1
 
< 0.1%
0.098621403 1
 
< 0.1%
0.120699914 1
 
< 0.1%
0.129676893 1
 
< 0.1%
0.131837013 1
 
< 0.1%
0.139200623 1
 
< 0.1%
0.150650498 1
 
< 0.1%
ValueCountFrequency (%)
361.7637419 1
< 0.1%
360.9533839 1
< 0.1%
287.9537928 1
< 0.1%
270.7846931 1
< 0.1%
261.4912857 1
< 0.1%
258.5498732 1
< 0.1%
255.5691579 1
< 0.1%
254.6071579 1
< 0.1%
246.7585902 1
< 0.1%
239.98 1
< 0.1%

SpecialDay
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061972293
Minimum0
Maximum1
Zeros10950
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:01.788604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.19971059
Coefficient of variation (CV)3.2225787
Kurtosis9.790558
Mean0.061972293
Median Absolute Deviation (MAD)0
Skewness3.2848845
Sum756
Variance0.039884321
MonotonicityNot monotonic
2024-04-14T20:03:02.194734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10950
89.8%
0.6 350
 
2.9%
0.8 324
 
2.7%
0.4 243
 
2.0%
0.2 178
 
1.5%
1 154
 
1.3%
ValueCountFrequency (%)
0 10950
89.8%
0.2 178
 
1.5%
0.4 243
 
2.0%
0.6 350
 
2.9%
0.8 324
 
2.7%
1 154
 
1.3%
ValueCountFrequency (%)
1 154
 
1.3%
0.8 324
 
2.7%
0.6 350
 
2.9%
0.4 243
 
2.0%
0.2 178
 
1.5%
0 10950
89.8%

Month
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6671858
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:02.569712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median8
Q311
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.386929
Coefficient of variation (CV)0.44174344
Kurtosis-1.6130291
Mean7.6671858
Median Absolute Deviation (MAD)3
Skewness-0.062285125
Sum93532
Variance11.471288
MonotonicityNot monotonic
2024-04-14T20:03:03.007217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 3327
27.3%
11 2979
24.4%
3 1860
15.2%
12 1706
14.0%
10 549
 
4.5%
9 448
 
3.7%
8 433
 
3.5%
7 432
 
3.5%
6 284
 
2.3%
2 181
 
1.5%
ValueCountFrequency (%)
2 181
 
1.5%
3 1860
15.2%
5 3327
27.3%
6 284
 
2.3%
7 432
 
3.5%
8 433
 
3.5%
9 448
 
3.7%
10 549
 
4.5%
11 2979
24.4%
12 1706
14.0%
ValueCountFrequency (%)
12 1706
14.0%
11 2979
24.4%
10 549
 
4.5%
9 448
 
3.7%
8 433
 
3.5%
7 432
 
3.5%
6 284
 
2.3%
5 3327
27.3%
3 1860
15.2%
2 181
 
1.5%

OperatingSystems
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1244364
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:03.413417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90683878
Coefficient of variation (CV)0.42686087
Kurtosis10.280976
Mean2.1244364
Median Absolute Deviation (MAD)0
Skewness2.0331587
Sum25916
Variance0.82235657
MonotonicityNot monotonic
2024-04-14T20:03:03.835189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6539
53.6%
1 2546
 
20.9%
3 2529
 
20.7%
4 478
 
3.9%
8 75
 
0.6%
6 19
 
0.2%
7 7
 
0.1%
5 6
 
< 0.1%
ValueCountFrequency (%)
1 2546
 
20.9%
2 6539
53.6%
3 2529
 
20.7%
4 478
 
3.9%
5 6
 
< 0.1%
6 19
 
0.2%
7 7
 
0.1%
8 75
 
0.6%
ValueCountFrequency (%)
8 75
 
0.6%
7 7
 
0.1%
6 19
 
0.2%
5 6
 
< 0.1%
4 478
 
3.9%
3 2529
 
20.7%
2 6539
53.6%
1 2546
 
20.9%

Browser
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3582261
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:04.272711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile5
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7103926
Coefficient of variation (CV)0.7252878
Kurtosis12.547866
Mean2.3582261
Median Absolute Deviation (MAD)0
Skewness3.2167985
Sum28768
Variance2.9254429
MonotonicityNot monotonic
2024-04-14T20:03:04.694478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 7880
64.6%
1 2424
 
19.9%
4 731
 
6.0%
5 465
 
3.8%
6 174
 
1.4%
10 163
 
1.3%
8 135
 
1.1%
3 105
 
0.9%
13 56
 
0.5%
7 49
 
0.4%
Other values (3) 17
 
0.1%
ValueCountFrequency (%)
1 2424
 
19.9%
2 7880
64.6%
3 105
 
0.9%
4 731
 
6.0%
5 465
 
3.8%
6 174
 
1.4%
7 49
 
0.4%
8 135
 
1.1%
9 1
 
< 0.1%
10 163
 
1.3%
ValueCountFrequency (%)
13 56
 
0.5%
12 10
 
0.1%
11 6
 
< 0.1%
10 163
 
1.3%
9 1
 
< 0.1%
8 135
 
1.1%
7 49
 
0.4%
6 174
 
1.4%
5 465
3.8%
4 731
6.0%

Region
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1512419
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:05.085059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4007083
Coefficient of variation (CV)0.76182927
Kurtosis-0.1565141
Mean3.1512419
Median Absolute Deviation (MAD)2
Skewness0.97947318
Sum38442
Variance5.7634004
MonotonicityNot monotonic
2024-04-14T20:03:05.523925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4714
38.6%
3 2378
19.5%
4 1171
 
9.6%
2 1128
 
9.2%
6 801
 
6.6%
7 757
 
6.2%
9 503
 
4.1%
8 429
 
3.5%
5 318
 
2.6%
ValueCountFrequency (%)
1 4714
38.6%
2 1128
 
9.2%
3 2378
19.5%
4 1171
 
9.6%
5 318
 
2.6%
6 801
 
6.6%
7 757
 
6.2%
8 429
 
3.5%
9 503
 
4.1%
ValueCountFrequency (%)
9 503
 
4.1%
8 429
 
3.5%
7 757
 
6.2%
6 801
 
6.6%
5 318
 
2.6%
4 1171
 
9.6%
3 2378
19.5%
2 1128
 
9.2%
1 4714
38.6%

TrafficType
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0726289
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:05.915041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0153996
Coefficient of variation (CV)0.98594783
Kurtosis3.4672492
Mean4.0726289
Median Absolute Deviation (MAD)1
Skewness1.9587751
Sum49682
Variance16.123434
MonotonicityNot monotonic
2024-04-14T20:03:06.352562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 3910
32.1%
1 2388
19.6%
3 2011
16.5%
4 1066
 
8.7%
13 728
 
6.0%
10 450
 
3.7%
6 442
 
3.6%
8 342
 
2.8%
5 260
 
2.1%
11 247
 
2.0%
Other values (10) 355
 
2.9%
ValueCountFrequency (%)
1 2388
19.6%
2 3910
32.1%
3 2011
16.5%
4 1066
 
8.7%
5 260
 
2.1%
6 442
 
3.6%
7 40
 
0.3%
8 342
 
2.8%
9 41
 
0.3%
10 450
 
3.7%
ValueCountFrequency (%)
20 193
 
1.6%
19 17
 
0.1%
18 9
 
0.1%
17 1
 
< 0.1%
16 3
 
< 0.1%
15 37
 
0.3%
14 13
 
0.1%
13 728
6.0%
12 1
 
< 0.1%
11 247
 
2.0%

Weekend
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.4 KiB
0
9341 
1
2858 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12199
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Length

2024-04-14T20:03:06.790025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:03:07.446205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12199
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
Common 12199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9341
76.6%
1 2858
 
23.4%

Revenue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.4 KiB
0
10291 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12199
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

Length

2024-04-14T20:03:07.806328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:03:08.150116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12199
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10291
84.4%
1 1908
 
15.6%

VisitorType_New_Visitor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.4 KiB
0
10506 
1
1693 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12199
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

Length

2024-04-14T20:03:08.509525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:03:08.853370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12199
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10506
86.1%
1 1693
 
13.9%

VisitorType_Other
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.4 KiB
0
12118 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12199
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

Length

2024-04-14T20:03:09.213083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:03:09.541123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12199
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12118
99.3%
1 81
 
0.7%

VisitorType_Returning_Visitor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.4 KiB
1
10425 
0
1774 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12199
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Length

2024-04-14T20:03:09.916590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T20:03:10.245043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Most occurring characters

ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12199
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 12199
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10425
85.5%
0 1774
 
14.5%

Administrative_Proportion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6514
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.081457984
Minimum0
Maximum1
Zeros5637
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:10.651724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0079115315
Q30.09095333
95-th percentile0.40947613
Maximum1
Range1
Interquartile range (IQR)0.09095333

Descriptive statistics

Standard deviation0.15294876
Coefficient of variation (CV)1.8776398
Kurtosis9.5852688
Mean0.081457984
Median Absolute Deviation (MAD)0.0079115315
Skewness2.8844008
Sum993.70595
Variance0.023393322
MonotonicityNot monotonic
2024-04-14T20:03:11.167313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5637
46.2%
1 24
 
0.2%
0.5 9
 
0.1%
0.2142857143 3
 
< 0.1%
0.1346153846 3
 
< 0.1%
0.1866028708 2
 
< 0.1%
0.01652892562 2
 
< 0.1%
0.2445820433 2
 
< 0.1%
0.3055555556 2
 
< 0.1%
0.003676470588 2
 
< 0.1%
Other values (6504) 6513
53.4%
ValueCountFrequency (%)
0 5637
46.2%
0.0001385104452 1
 
< 0.1%
0.0001468553612 1
 
< 0.1%
0.0001714138788 1
 
< 0.1%
0.0001778536871 1
 
< 0.1%
0.0001863657966 1
 
< 0.1%
0.0002066226881 1
 
< 0.1%
0.0002188500505 1
 
< 0.1%
0.0002297980342 1
 
< 0.1%
0.0002553258338 1
 
< 0.1%
ValueCountFrequency (%)
1 24
0.2%
0.9981498612 1
 
< 0.1%
0.9960238569 1
 
< 0.1%
0.9863094074 1
 
< 0.1%
0.9816176471 1
 
< 0.1%
0.9777777778 1
 
< 0.1%
0.9768518519 1
 
< 0.1%
0.9732121923 1
 
< 0.1%
0.9731903485 1
 
< 0.1%
0.9723553262 1
 
< 0.1%

Informational_Proportion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2621
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01971114
Minimum0
Maximum1
Zeros9568
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:11.652472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1161933
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.074471297
Coefficient of variation (CV)3.7781324
Kurtosis53.501145
Mean0.01971114
Median Absolute Deviation (MAD)0
Skewness6.4218588
Sum240.4562
Variance0.0055459741
MonotonicityNot monotonic
2024-04-14T20:03:12.152985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9568
78.4%
1 7
 
0.1%
0.5 4
 
< 0.1%
0.02222222222 2
 
< 0.1%
0.001015073847 2
 
< 0.1%
0.01692479633 1
 
< 0.1%
0.0004945858087 1
 
< 0.1%
0.009074867658 1
 
< 0.1%
0.1980720632 1
 
< 0.1%
0.006071757129 1
 
< 0.1%
Other values (2611) 2611
 
21.4%
ValueCountFrequency (%)
0 9568
78.4%
8.686158295 × 10-51
 
< 0.1%
8.879293798 × 10-51
 
< 0.1%
9.941988678 × 10-51
 
< 0.1%
0.0001197479605 1
 
< 0.1%
0.0001284527332 1
 
< 0.1%
0.0001293329549 1
 
< 0.1%
0.0001339224789 1
 
< 0.1%
0.0001389310601 1
 
< 0.1%
0.0001508798279 1
 
< 0.1%
ValueCountFrequency (%)
1 7
0.1%
0.9655172414 1
 
< 0.1%
0.9652063119 1
 
< 0.1%
0.9473684211 1
 
< 0.1%
0.9166666667 1
 
< 0.1%
0.8435350624 1
 
< 0.1%
0.8423033496 1
 
< 0.1%
0.8347266881 1
 
< 0.1%
0.812208523 1
 
< 0.1%
0.7927272727 1
 
< 0.1%

ProductRelated_Proportion
Real number (ℝ)

HIGH CORRELATION 

Distinct6965
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89883088
Minimum0
Maximum1
Zeros32
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size95.4 KiB
2024-04-14T20:03:12.637319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.51530636
Q10.87270969
median0.98236902
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.12729031

Descriptive statistics

Standard deviation0.17207885
Coefficient of variation (CV)0.19144742
Kurtosis6.6358923
Mean0.89883088
Median Absolute Deviation (MAD)0.017630977
Skewness-2.4625277
Sum10964.838
Variance0.02961113
MonotonicityNot monotonic
2024-04-14T20:03:13.153059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5174
42.4%
0 32
 
0.3%
0.5 11
 
0.1%
0.7857142857 3
 
< 0.1%
0.9811320755 2
 
< 0.1%
0.03424657534 2
 
< 0.1%
0.08333333333 2
 
< 0.1%
0.7554179567 2
 
< 0.1%
0.6111111111 2
 
< 0.1%
0.9259259259 2
 
< 0.1%
Other values (6955) 6967
57.1%
ValueCountFrequency (%)
0 32
0.3%
0.00185013876 1
 
< 0.1%
0.003976143141 1
 
< 0.1%
0.005018820577 1
 
< 0.1%
0.01369059261 1
 
< 0.1%
0.01838235294 1
 
< 0.1%
0.02146427521 1
 
< 0.1%
0.02222222222 1
 
< 0.1%
0.02314814815 1
 
< 0.1%
0.02678780774 1
 
< 0.1%
ValueCountFrequency (%)
1 5174
42.4%
0.9998221463 1
 
< 0.1%
0.9997825413 1
 
< 0.1%
0.9997811499 1
 
< 0.1%
0.9997810493 1
 
< 0.1%
0.999770202 1
 
< 0.1%
0.9997688255 1
 
< 0.1%
0.9997557658 1
 
< 0.1%
0.9997533357 1
 
< 0.1%
0.9996438955 1
 
< 0.1%

Interactions

2024-04-14T20:02:43.485065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:52.905548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:57.000344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:03.937884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:10.079016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:16.676558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:23.381722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:30.179521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:37.070673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:43.916131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:50.558491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:56.980990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:03.923813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:10.458908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:17.242277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:23.820652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:30.447322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:37.248968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:43.813160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.030937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:57.375316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:04.250577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:10.422648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:17.255165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:23.709792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:30.538803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:37.414716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:44.291077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:50.886636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:57.293447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:04.330026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:10.786987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:17.648436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:24.164685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:30.806687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:37.577059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:44.172493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.156023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:57.781519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:04.609911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:10.813679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:17.630215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:24.069115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:30.944993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:37.790109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:44.682050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:51.245991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:57.683979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:04.704987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:11.146523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:18.070677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:24.603157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:31.181605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:37.936386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:44.500584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.265352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:58.127421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:04.922401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:11.173628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:17.958303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:24.412822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:31.288772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:38.149449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:45.072634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:51.590459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:58.043869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:05.033074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:11.459071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:18.398072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:24.947102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:31.790924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:38.264508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:44.859926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.390356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:58.646477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:05.250472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:11.532933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:18.302016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:24.756562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:31.648107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:38.508718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:45.447579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:51.919002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:58.387525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:05.376787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:12.052754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:18.741795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:25.290831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:32.150310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:38.608219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:45.203636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.530980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:59.037045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:05.594190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:11.923893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:18.661497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:25.115905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:32.023071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:38.883749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:45.854958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:52.528259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:58.778474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:05.736316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:12.396466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:19.101454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:25.650162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:32.525213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:38.951916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:45.562903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.702749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:59.396445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:05.937834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:12.298849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:19.020828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:25.459541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:32.413638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:39.258711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:46.214251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:52.872042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:59.153715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:06.095880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:12.724697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:19.460796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:26.009489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:32.915797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:39.311193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:45.969110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:53.874613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:59.850478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:06.297239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:12.689433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:19.395770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:25.913009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:32.804245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:39.649679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:46.636074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:53.247004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:59.559930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:06.565916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:13.099999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:19.867274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:26.400089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:33.353259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:39.702187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:46.375323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:54.062191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:00.258057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:06.672210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:13.080058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:19.786530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:26.319288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:33.179193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:40.024655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:47.027053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:53.606271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:59.982020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:06.941823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:13.459402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:20.273462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:26.790849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:33.759864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:40.077263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:46.750280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:54.218320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:00.648638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:07.000629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:13.501917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:20.130851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:26.725436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:33.569791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:40.415701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:47.386659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:53.965683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:00.372916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:07.301272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:13.866064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:20.648439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:27.165994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:34.121575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:40.421876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:47.125250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:54.358950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:01.007962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:07.344391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:13.878685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:20.537692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:27.100404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:33.913452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:40.759742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:47.730351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:54.278091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:00.732323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:07.661306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:14.209832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:20.992151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:27.509711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:34.465239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:40.749975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:47.516302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:54.515219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:01.367553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:07.657369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:14.207375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:20.881394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:27.491379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:34.272804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:41.119090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:48.058471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:54.606226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:01.092542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:07.989760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:14.710772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:21.336516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:27.838271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:34.824592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:41.077983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:47.906958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:54.749562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:01.759316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:08.001078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:14.598359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:21.256865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:27.881979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:34.882148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:41.493985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:48.433780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:54.965563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:01.499099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:08.333469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:15.085736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:21.695852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:28.213232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:35.183985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:41.437322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:48.281860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:55.108940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:02.109483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:08.329166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:14.942134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:21.584991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:28.256921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:35.241489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:41.885078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:48.761870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:55.293650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:01.889683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:08.692792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:15.429453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:22.039570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:28.541322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:35.512099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:41.765471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:48.656805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:55.499549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:02.500100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:08.672904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:15.317096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:21.959975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:28.647498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:35.616474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:42.306891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:49.136819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:55.652990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:02.358417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:09.052781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:15.820033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:22.398893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:28.931848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:35.871906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:42.124751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:49.063014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:55.890639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:02.874987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:09.016612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:15.660763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:22.334937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:29.053707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:35.991604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:42.713095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:49.511796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:55.996711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:02.796691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:09.427799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:16.194987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:22.758229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:29.291251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:36.247410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:42.484231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:49.453600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:56.265582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:03.234399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:09.344634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:16.004469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:22.678663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:29.444301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:36.351469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:43.103689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:49.855510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:56.324795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:03.189368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:09.771466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:16.554811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:23.117524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:29.666145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:36.576396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:42.812836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:49.815602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:00:56.640552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:03.578112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:09.704018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:16.348479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:23.006727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:29.834884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:36.711355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:43.509907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:50.199221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:01:56.652901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:03.548718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:10.115245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:16.882987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:23.461272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:30.072342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:36.904667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T20:02:43.156549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T20:03:13.559266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeAdministrative_ProportionInformational_ProportionProductRelated_ProportionWeekendRevenueVisitorType_New_VisitorVisitorType_OtherVisitorType_Returning_Visitor
Administrative1.0000.9450.3670.3660.4520.412-0.139-0.4250.325-0.1290.077-0.007-0.0150.007-0.0150.8810.353-0.8300.0250.1280.1220.0000.120
Administrative_Duration0.9451.0000.3560.3560.4230.406-0.146-0.4270.316-0.1370.078-0.010-0.0260.016-0.0180.9250.343-0.8700.0000.0630.0200.0000.022
Informational0.3670.3561.0000.9920.3660.3640.016-0.1790.217-0.0560.057-0.001-0.022-0.024-0.0310.2330.989-0.4330.0100.0760.0440.0000.047
Informational_Duration0.3660.3560.9921.0000.3660.3660.013-0.1850.221-0.0550.052-0.000-0.018-0.022-0.0320.2310.996-0.4410.0000.0670.0280.0000.029
ProductRelated0.4520.4230.3660.3661.0000.879-0.021-0.5040.338-0.0270.1360.0190.040-0.025-0.0770.2170.338-0.2340.0000.1250.1110.0070.115
ProductRelated_Duration0.4120.4060.3640.3660.8791.000-0.048-0.4600.356-0.0550.1300.0210.042-0.013-0.0800.1700.334-0.1900.0040.0710.0550.0000.055
BounceRates-0.139-0.1460.0160.013-0.021-0.0481.0000.589-0.1160.1430.0040.058-0.043-0.0150.022-0.1880.0080.1740.0420.1650.1630.0410.163
ExitRates-0.425-0.427-0.179-0.185-0.504-0.4600.5891.000-0.3040.159-0.0600.026-0.012-0.0000.029-0.376-0.1790.3710.0590.2410.2540.0350.249
PageValues0.3250.3160.2170.2210.3380.356-0.116-0.3041.000-0.0720.060-0.0140.0240.000-0.0200.2100.208-0.2140.0310.4130.1160.1040.126
SpecialDay-0.129-0.137-0.056-0.055-0.027-0.0550.1430.159-0.0721.000-0.2560.0220.020-0.0150.111-0.135-0.0530.1330.2600.0870.0890.0190.093
Month0.0770.0780.0570.0520.1360.1300.004-0.0600.060-0.2561.0000.004-0.0240.014-0.0240.0520.046-0.0560.0530.1720.1220.1390.140
OperatingSystems-0.007-0.010-0.001-0.0000.0190.0210.0580.026-0.0140.0220.0041.0000.3700.0260.079-0.024-0.0020.0270.1170.0750.0480.6390.147
Browser-0.015-0.026-0.022-0.0180.0400.042-0.043-0.0120.0240.020-0.0240.3701.0000.055-0.002-0.037-0.0210.0410.0580.0400.0370.6540.131
Region0.0070.016-0.024-0.022-0.025-0.013-0.015-0.0000.000-0.0150.0140.0260.0551.000-0.0080.021-0.020-0.0140.0150.0110.0310.2410.082
TrafficType-0.015-0.018-0.031-0.032-0.077-0.0800.0220.029-0.0200.111-0.0240.079-0.002-0.0081.0000.009-0.028-0.0050.0900.1200.2400.3610.243
Administrative_Proportion0.8810.9250.2330.2310.2170.170-0.188-0.3760.210-0.1350.052-0.024-0.0370.0210.0091.0000.233-0.9350.0250.0440.2570.0410.257
Informational_Proportion0.3530.3430.9890.9960.3380.3340.008-0.1790.208-0.0530.046-0.002-0.021-0.020-0.0280.2331.000-0.4510.0280.0340.0220.0000.025
ProductRelated_Proportion-0.830-0.870-0.433-0.441-0.234-0.1900.1740.371-0.2140.133-0.0560.0270.041-0.014-0.005-0.935-0.4511.0000.0340.0780.2330.0210.231
Weekend0.0250.0000.0100.0000.0000.0040.0420.0590.0310.2600.0530.1170.0580.0150.0900.0250.0280.0341.0000.0260.0450.0260.037
Revenue0.1280.0630.0760.0670.1250.0710.1650.2410.4130.0870.1720.0750.0400.0110.1200.0440.0340.0780.0261.0000.1020.0000.102
VisitorType_New_Visitor0.1220.0200.0440.0280.1110.0550.1630.2540.1160.0890.1220.0480.0370.0310.2400.2570.0220.2330.0450.1021.0000.0300.973
VisitorType_Other0.0000.0000.0000.0000.0070.0000.0410.0350.1040.0190.1390.6390.6540.2410.3610.0410.0000.0210.0260.0000.0301.0000.197
VisitorType_Returning_Visitor0.1200.0220.0470.0290.1150.0550.1630.2490.1260.0930.1400.1470.1310.0820.2430.2570.0250.2310.0370.1020.9730.1971.000

Missing values

2024-04-14T20:02:50.378114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T20:02:51.534324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeWeekendRevenueVisitorType_New_VisitorVisitorType_OtherVisitorType_Returning_VisitorAdministrative_ProportionInformational_ProportionProductRelated_Proportion
000.000.011.0000000.2000000.2000000.00.021111000010.00.01.0
100.000.0264.0000000.0000000.1000000.00.022212000010.00.01.0
200.000.011.0000000.2000000.2000000.00.024193000010.00.01.0
300.000.022.6666670.0500000.1400000.00.023224000010.00.01.0
400.000.010627.5000000.0200000.0500000.00.023314100010.00.01.0
500.000.019154.2166670.0157890.0245610.00.022213000010.00.01.0
600.000.011.0000000.2000000.2000000.00.422433000010.00.01.0
711.000.000.0000000.2000000.2000000.00.021215100011.00.00.0
800.000.0237.0000000.0000000.1000000.00.822223000010.00.01.0
900.000.03738.0000000.0000000.0222220.00.422412000010.00.01.0
AdministrativeAdministrative_DurationInformationalInformational_DurationProductRelatedProductRelated_DurationBounceRatesExitRatesPageValuesSpecialDayMonthOperatingSystemsBrowserRegionTrafficTypeWeekendRevenueVisitorType_New_VisitorVisitorType_OtherVisitorType_Returning_VisitorAdministrative_ProportionInformational_ProportionProductRelated_Proportion
1218900.0000.08143.5833330.0142860.0500000.0000000.0112231000010.0000000.0000001.000000
1219000.0000.061.0000000.2000000.2000000.0000000.0111841000010.0000000.0000001.000000
12191676.2500.0221075.2500000.0000000.0041670.0000000.0122242000010.0662180.0000000.933782
12192264.7500.0441157.9761900.0000000.0139530.0000000.01122110000010.0529550.0000000.947045
1219300.0011.016503.0000000.0000000.0376470.0000000.0112211000010.0000000.0019840.998016
121943145.0000.0531783.7916670.0071430.02903112.2417170.0124611100010.0751770.0000000.924823
1219500.0000.05465.7500000.0000000.0213330.0000000.0113218100010.0000000.0000001.000000
1219600.0000.06184.2500000.0833330.0866670.0000000.01132113100010.0000000.0000001.000000
12197475.0000.015346.0000000.0000000.0210530.0000000.01122311000010.1781470.0000000.821853
1219800.0000.0321.2500000.0000000.0666670.0000000.0113212101000.0000000.0000001.000000